In an electronic era, computing systems play an important role in transforming data into useful information to derive a meaningful output. Examples of the data may include a text data, an image data, a video data and an audio data. The text data constitutes significant and influential content available on the Web and other data repositories. In one aspect, the text data may be received from a plurality of users accessing through the web by using a computing system. Example of the text data may comprise responses to surveys conducted on the Internet or intranet. In such scenario, the text data may be relevant in order to derive statistical inferences from the responses. In addition, some of the other major sources of the data that may be used for deriving the statistical inference are social platforms. Examples of the social platforms, may include, but not limited to, such as Facebook™, Twitter™, and LinkedIn®. However, it has been observed that, the text data in the form of the responses and the data received from the plurality of users may use colloquial or informal language in order to convey feelings share thoughts; provide opinions, suggestions and the like on the social platform. It may be understood that, the colloquial or informal language and tendency to use non-standard shorter forms of words, abbreviations etc. may be attributed to the advent of modern technologies of communication such as SMS (short messaging service), or ‘SWYPE1®’. The SWYPE1® provides a method for using the colloquial or informal language through a hand held device for instant messaging/chatting on various web tools (such as Google's GTalk, Yahoo Messenger). In one aspect, the SWYPE1® enables the users to minimize or use fewer amounts of key-strokes that are needed to perform for conveying feelings sharing thoughts, providing opinions or suggestions. For example, the users may use: “gud” for “good”, “pkg” for “package”, “gr8” for “great”, “4 u” for “for you”, “LOL” for “Laughing out Loud”, or “IMHO” for “In My Humble Opinion”.
In addition to the social platforms, a lot of surveys are frequently conducted to derive the statistical inferences about population being studied. In order to make the statistical inferences, survey methodologies have been applied to collect data of individuals from the population. The data may be collected from the individual in the form of responses for a set of questionnaire. It may be understood that, the responses may be in the form of textual data that may contain certain typographical errors. Therefore, the informal/colloquial and the typographical error in the textual data need to be rectified before it is amenable for further automated text analysis in order to make statistical inferences.
However, various typographical error rectification methods and systems have been implemented that are aimed towards rectifying the typographical error. Some such systems for rectifying the typographical error are Microsoft® Word's spelling and grammar checker as well as open source like Open Office Writer, HunSpell, and Gnu Aspell. In such systems, each word in the text file may be compared with a lexicon to provide a list of possible replacement for the word that is deemed to be identified as a candidate word having the typographical error. In order to rectify the candidate word using such systems, the user has to intervene so as to decide if the candidate word is indeed having the typographical error. If the user identified that the candidate word is having the typographical error, an appropriate spelling correction from the list of suggested corrections by such systems may be selected by the user. It may be understood that, if the appropriate spelling correction may not present in the list, then user may correct the typographical error of the candidate word.